On the Thinking-Language Modeling Gap in Large Language Models
Hong Kong Baptist University（香港 Baptist 大学）；MBZUAI Carnegie Mellon University（MBZUAI 美国卡内基梅隆大学）；The University of Sydney（悉尼大学）；The Chinese University of Hong Kong（香港中文大学）
Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang
System 2 reasoning is one of the defining characteristics of intelligence, which requires slow and logical thinking. Human conducts System 2 reasoning via the language of thoughts that organizes the reasoning process as a causal sequence of mental language, or thoughts. Recently, it has been observed that System 2 reasoning can be elicited from Large Language Models (LLMs) pre-trained on large-scale natural languages. However, in this work, we show that there is a significant gap between the modeling of languages and thoughts. As language is primarily a tool for humans to share knowledge and thinking, modeling human language can easily absorb language biases into LLMs deviated from the chain of thoughts in minds. Furthermore, we show that the biases will mislead the eliciting of "thoughts" in LLMs to focus only on a biased part of the premise. To this end, we propose a new prompt technique termed Language-of-Thoughts (LoT) to demonstrate and alleviate this gap. Instead of directly eliciting the chain of thoughts from partial information, LoT instructs LLMs to adjust the order and token used for the expressions of all the relevant information. We show that the simple strategy significantly reduces the language modeling biases in LLMs and improves the performance of LLMs across a variety of reasoning tasks.
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Information Sciences Institute, Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院信息科学研究所）；Department of Computer Science, Faculty of Science, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学理学院计算机科学系）；Tencent AI Lab（腾讯AI实验室）
PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
Center for Computational Science and Engineering（计算科学与工程中心）；Schmarzman College of Computing（施马茨曼计算学院）；Laboratory for Atomistic and Molecular Mechanics (LAMM)（原子与分子力学实验室）；Massachusetts Institute of Technology（麻省理工学院）
Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（清华大学计算机科学与技术系、人工智能研究院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Tencent（腾讯）
RAIN-Merging: A Gradient-Free Method to Enhance Instruction Following in Large Reasoning Models with Preserved Thinking Format
Institute of Image Processing and Pattern Recoginition, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（图像处理与模式识别研究所，自动化与智能感知学院，上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；MoE Key Laboratory of System Control and Information Processing (Shanghai)（系统控制与信息处理国家重点实验室（上海））
Thinking with Tables: Enhancing Multi-Modal Tabular Understanding via Neuro-Symbolic Reasoning
State Key Laboratory of Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学，中国）；School of Artificial Intelligence, Nanjing University, China（人工智能学院，南京大学，中国）；School of Intelligence Science and Technology, Nanjing University, China（智能科学与技术学院，南京大学，中国）
Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
Thinking with Drafting: Optical Decompression via Logical Reconstruction
Shenyang Institute of Computing Technology, Chinese Academy of Sciences（中国科学院沈阳计算技术研究所）；University of Chinese Academy of Sciences 3 ByteDance 4 Westlake University（中国科学院大学 3 字节跳动 4 西湖大学）；Key Laboratory of Computing Power Network（计算功率网络重点实验室）；Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（信息安全，教育部，山东计算机科学中心（济南国家超级计算中心），齐鲁工业大学（山东省科学院））
H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking
Duke University（杜克大学）；Accenture（埃森哲）；Accenture Security（埃森哲安全）
Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Bank of China（中国银行）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）；State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所非线性力学重点实验室）
Interleaving Reasoning for Better Text-to-Image Generation
East China Normal University（华东师范大学）；The Chinese University of Hong Kong（香港中文大学）；Xiaohongshu Inc.（小红书公司）；University of California, Los Angeles（加州大学洛杉矶分校）；Zhejiang University（浙江大学）；University of Oxford（牛津大学）
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking
Institute of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Information Research Center of Military Science, PLA Academy of Military Science（军事科学院军事科学信息研究中心）
A Joint Spectro-Temporal Relational Thinking Based Acoustic Modeling Framework
School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气与通信工程学院）；School of Computing and Information Systems, University of Melbourne（墨尔本大学计算与信息系统学院）
Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克技术大学电气工程学院）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（布拉格捷克技术大学捷克信息学、机器人学与控制论研究所）
Language Models as Critical Thinking Tools: A Case Study of Philosophers
Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）
Mila（米拉）；Université de Montréal（蒙特利尔大学）；Microsoft Research Montréal（微软蒙特利尔研究院）；McGill University（麦吉尔大学）
Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments sho…
When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy
University of Groningen（格罗宁根大学）；Harvard University（哈佛大学）；Mass General Brigham（麻省总医院）；Boston Children’s Hospital（波士顿儿童医院）；University of Amsterdam（阿姆斯特丹大学）
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Dolby Laboratories（杜比实验室）
